Leveraging machine learning for identify hydrological extreme events under global climate change (Proposals Track)

Ying-Jung C Deweese (Georgia Insititute of Technology)



Hydrological extreme events, such as droughts and floods, are highly destructive 1 natural disasters and its occurrence is expected to increase under the future climate change. Accurate and efficient approach to detect such events will provide timely information to assist management strategies for minimizing socio-economic damages. Despite the threshold approach has established to detect extreme events, the missing data from hydroclimate data and accurately identifying these events are still major challenges. The advent of machine learning models can help to identify the occurrence of droughts and floods events accurately and efficiently. Therefore, this proposed study will develop a machine learning model with semi-supervised anomaly detection approach to identify hydrological extreme events with ground-based data. As a test case, we will use 45-years record of hydroclimate data in coastal California, where was the driest region in 2012-2015, following with flash floods events. The expected results will increase communities’ awareness for hydrological extreme events and enable environmental planning and resource management under climate change